https://github.com/moses000/mysoftware-nocnetintel
AI-powered NOC assistant for forecasting network outages, analyzing root causes, and recommending proactive resolutions using LLM and time-series intelligence.
https://github.com/moses000/mysoftware-nocnetintel
ai data-pipeline deep-learning docker forecasting llm mlops network-operations nlp noc postgresql predictive-maintenance pytorch root-cause-analysis telecom time-series unsupervised-learning
Last synced: 5 months ago
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AI-powered NOC assistant for forecasting network outages, analyzing root causes, and recommending proactive resolutions using LLM and time-series intelligence.
- Host: GitHub
- URL: https://github.com/moses000/mysoftware-nocnetintel
- Owner: moses000
- License: gpl-3.0
- Created: 2025-05-08T07:32:06.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-08T07:55:57.000Z (5 months ago)
- Last Synced: 2025-05-12T08:06:55.145Z (5 months ago)
- Topics: ai, data-pipeline, deep-learning, docker, forecasting, llm, mlops, network-operations, nlp, noc, postgresql, predictive-maintenance, pytorch, root-cause-analysis, telecom, time-series, unsupervised-learning
- Homepage:
- Size: 21.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🧠 Noc-netIntel – AI-Powered Network Operations Intelligence Assistant
**Noc-netIntel** is an intelligent chat-driven platform designed to forecast critical network outages, provide possible root cause explanations, suggest proactive FME (Field Maintenance Engineer) deployment, and recommend resolutions — all powered by advanced AI, NLP, and time-series forecasting.
## 💡 What It Does
- 🔮 **Predicts outages** today, tomorrow, and over the week
- 📉 **Identifies root causes** using LSTM + LLM reasoning
- 📅 **Schedules field engineers** proactively
- 🧠 **Suggests resolutions** from a growing knowledge base
- 💬 **Conversational interface** with LLM (DeepSeek/OpenAI-compatible)
## 🛠 Technology Stack
| Layer | Tech |
|------------------------|-------------------------------------|
| **AI/NLP** | DeepSeek / Custom LLM |
| **ML/Forecasting** | PyTorch + Custom LSTM |
| **Backend** | Python (FastAPI preferred) |
| **Frontend** | JavaScript (React recommended) |
| **Database** | PostgreSQL |
| **Data Pipeline** | Python Scripts / Celery Tasks |
| **Deployment** | Docker & Docker Compose |
| **Scheduler (optional)**| Celery + Redis for task management |
## 🧬 End-to-End Workflow
### 1. 🔗 Data Collection
- Sources: Sensor logs, BTS data, voltage/current levels, historical tickets, alarms
- Stored in PostgreSQL (structured) and optional object storage (raw logs)
### 2. 🧹 Data Preprocessing
- Cleansing missing/nulls, noise filtering
- Timestamp alignment, interpolation
- Scaling, encoding categorical signals (battery status, alarm type)
### 3. 🔧 Feature Engineering
- Temporal signals: time of day, day of week, holiday
- Environmental: power metrics, weather (optional)
- Historical: frequency of past outages, lag features
- Rolling stats: moving average, rate of failure
### 4. 📊 ML Forecasting (PyTorch + LSTM)
- Input: Sequence of multivariate time series
- Architecture: Multi-head LSTM → Dense heads (classification + regression)
- Outputs:
- Outage probability
- Affected region/site
- Possible root cause embeddings
- Metrics: F1, AUC for classification; RMSE for regression
### 5. 🧠 NLP Reasoning Layer (DeepSeek / LLM)
- Converts ML output into readable advice
- Enhances with historical patterns and predefined rules
- Formats chat response: outage + root cause + FME plan + resolution
### 6. 📅 Proactive FME Scheduler
- Ranks urgency and location clustering
- Optimizes FME routing using heuristic or ML-based dispatch
- Integrates with external calendars/ticketing if needed
## 💬 Sample Chat Interaction
**User**: "What outages are expected tomorrow in the North East zone?"
**Noc-netIntel**:
🛑 Predicted 3 possible outages:
* Site BGH-29 (Power drain) – 87% chance
* Site TMT-02 (Overload) – 72% chance
* Site JAK-10 (Backhaul degradation) – 55% chance
📌 Root Causes: Battery degradation, high load demand, backhaul link instability
🛠 Recommended Actions: Pre-deploy backup power units, initiate remote checks
👷 FME Suggestion: Team Alpha, report at 06:30 AM
## 🚀 API Overview
- `POST /chat` – Accepts user prompt, returns AI-generated insight
- `GET /forecast` – Returns raw model prediction
- `GET /schedule` – Lists recommended FME deployments
- `GET /logs` – Access recent outage logs (if allowed)
> Full Swagger UI at: `http://localhost:8000/docs`
## 🗃 Sample PostgreSQL Schema
CREATE TABLE outage_forecasts (
id SERIAL PRIMARY KEY,
site_code TEXT,
prediction_date TIMESTAMP,
outage_probability FLOAT,
root_cause TEXT,
fme_plan TEXT,
resolution TEXT
);
## 🐳 Setup and Deployment
### ✅ Prerequisites
* Docker & Docker Compose
* Python 3.9+
* Node.js (for frontend)
### 📦 Running Locally
bash
git clone https://github.com/moses000/mysoftware-nocNetIntel
cd noc-netintel
# Run with Docker Compose
docker-compose up --build
> Services:
>
> * `backend`: FastAPI ML/NLP engine
> * `frontend`: React chat UI (optional)
> * `ml_worker`: PyTorch + model runner
> * `postgres`: SQL data store
## 🔐 Auth & Roles
* JWT-based auth
* Roles: Admin, Analyst, Engineer
* Granular data access policies
## 📊 Monitoring & Logging
* Optional: Add Grafana for real-time alert visualization
* Backend logs all predictions and user queries
* Alerts for model drift / threshold breaches
## ✍️ Wiki & Docs
* 📘 `docs/data-pipeline.md`: Ingestion, ETL, transformations
* 📘 `docs/model.md`: LSTM architecture, training notes
* 📘 \`docs